System for Monitoring Elastic Cloud-Based Computing Systems as a Service

ABSTRACT

Provided is a computing-system monitor configured to monitor a plurality of computing-systems each having a plurality of monitored computing-instances. The computing-system monitor may include a plurality of collectors, each collector executed by one of a plurality of monitored computing-instances, wherein the plurality of monitored computing-instances each are part of one of a plurality of separately monitored computing systems, and wherein each of the collectors is operable to output metrics of a corresponding monitored computing-instance executing that collector. The computing-system monitor may also include an analytics platform, the analytics platform having a plurality of analytic computing-instances, the analytics platform being operable to receive metrics output by the plurality of collectors, calculate statistics with the analytic computing-instances based on the received metrics, and output the calculated statistics.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of pending U.S. patentapplication Ser. No. 13/293,751 filed Nov. 10, 2011, the contents ofwhich are incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to computing systems, and morespecifically, to monitoring the operation of computing systems.

2. Description of the Related Art

Systems management programs are often used for monitoring groups ofcomputing devices, such as a group of personal computers deployed withina company's local area network. Generally, some systems managementprograms are configured to monitor the performance, usage,configuration, and network activity of each of the computing devices inthe system. Some such systems management programs obtain data fromprograms, referred to as agents, executed by each of the computingdevices. The agents gather data at the computing device, and the systemsmanagement program generally coordinates the operation of the agents byestablishing connections with the agents and requesting the agents toreport data back to the systems management program, often byperiodically polling the agents for data.

Generally, existing systems management programs are not well-suited formonitoring the operation of relatively large computing systems, multiplecomputing systems, or computing systems in which constituent computingdevices are frequently added or removed. Configuring system managementprograms is often relatively labor-intensive, as certain such programsrequire an operator to identify, and configure the program for, each newcomputing device added to the system. Further, relatively largecomputing systems or multiple computing systems generally yieldrelatively large amounts of data, as each computing device in the systemmay be an additional potential source of information to be monitored.

These inadequacies are particularly challenging for those monitoringcomputing systems in a data center or other scalable computing system,such as computing systems operating in a cloud-based virtual datacenter. Often such computing systems are designed to be scalable, suchthat new computing devices or virtual machines are provisioned based onthe load placed on the computing system. As a result, in some use cases,new computing devices or new virtual machines (that is, computinginstances of the computing system) are added and removed relativelyfrequently as demand fluctuates. These transient computing instances aredifficult for certain existing system management programs to effectivelymonitor, as the amount of data generated can be potentially relativelylarge and the new instances often go unnoticed and unmonitored by thesystems management program until the systems management program isreconfigured to establish a connection with the new computing instancesand request data from them. Further, systems management programs areoften configured by technicians with relatively specialized knowledge,but such persons are often not in the employ of entities operatingcloud-based virtual data centers, which are often specifically designedto be used by entities without specialized expertise in the operationand maintenance of such computing systems. Moreover, because suchcomputing systems are often accessed over the Internet, rather than alocal area network under the control of a single entity, the connectionbetween the systems management program and the monitored computinginstances is often less reliable, which can result in uneven data flowsthat could potentially overwhelm the systems management program or causedata to be lost. Finally, those operating computing systems often relyon those computing systems continuing to operate and perform withcertain characteristics without fail over relatively long periods oftime, for instance over months or years. Relatively short deviations inperformance or operation are therefore of interest to such users, butmany existing systems management programs either do not monitor dataindicative of performance with sufficient granularity or do not monitordata indicative of performance with frequency speed to inform users ofevents briefly affecting performance.

BRIEF DESCRIPTION OF THE DRAWINGS

The aspects of the present techniques will be better understood when theapplication is read in view of the following figures in which likenumbers indicate similar or identical elements:

FIG. 1 shows an embodiment of an analytics-platform computing system formonitoring a plurality of monitored computing systems;

FIG. 2 shows an embodiment of a collector executed on computinginstances of monitored computing systems of FIG. 1;

FIG. 3 shows an embodiment of a process for initiating a monitoringsession with an analytics platform from a computing instance to bemonitored;

FIG. 4 shows an embodiment of a process for outputting metrics of amonitored computing instance to an analytics platform;

FIG. 5 shows an embodiment of a process for preparing gathered data tobe transmitted to an analytics platform;

FIG. 6 shows an embodiment of a process for transmitting gathered dataindicative of performance of a monitored computing instance to ananalytics platform;

FIG. 7 shows details of the analytics-platform computing system of FIG.1;

FIG. 8 shows an embodiment of a receive engine of the analytics-platformcomputing system of FIG. 7;

FIG. 9 shows an embodiment of an analytics engine of theanalytics-platform computing system of FIG. 7;

FIG. 10 shows an embodiment of a web user interface engine of theanalytics-platform computing system of FIG. 7;

FIG. 11 shows an embodiment of a platform engine of theanalytics-platform computing system of FIG. 7;

FIG. 12 shows an embodiment of a process for analyzing data receivedfrom a monitored computing system; and

FIG. 13 is an example of a computing device.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit theinvention to the particular form disclosed, but to the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present invention, e.g., asdefined by the appended claims.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

FIG. 1 shows an embodiment of an analytics-platform computing systemthat may address some or all of the deficiencies described above. Incertain embodiments, as described below, the analytics-platformcomputing system 12 may be configured to output results within less than(or substantially less than, e.g., in real time) approximately 120seconds of when the events upon which the results are based occur, e.g.,an even occurring on a monitored computing instance. Further, someembodiments may be capable of monitoring a plurality of differentcomputing systems, each associated with a different account, for exampleon behalf of a plurality of different entities having accounts, suchthat monitoring is provided as a service to account holders who arerelieved of the burden of hosting a computer system management program.Some embodiments, as described below, may also be relatively easy toconfigure to monitor new computing instances added to a monitoredcomputing system because, in some instances, the new computing instancesmay initiate a monitoring session with the analytics platform and pushdata to the analytics platform, without the analytics platform beingpre-configured to communicate with each specific new computing instance.Additionally, in some embodiments described below, theanalytics-platform computing system 12 may be a scalable computingsystem operable to provision additional monitoring computing instances26 or other additional computing resources based upon need, therebypotentially reducing the hardware costs associated with the system. Notall embodiments, however, provide all of these benefits, as varioustrade-offs may be made using the techniques described herein in pursuitof other objectives, and some embodiments may provide other benefits,some of which are described below.

In the embodiment of FIG. 1, a computing environment 10 includes theanalytics-platform computing system 12; a plurality of monitoredcomputing systems 14, 16, and 18; a plurality of client devices 20, 22,and 24; and a network 25. The illustrated analytics-platform computingsystem 12 includes a plurality of monitoring computing instances 26which may serve a variety of different functions, examples of which aredescribed below with reference to FIG. 7, and the number of which may bevariable based on the computing load placed on the analytics-platformcomputing system 12, as described below with reference to FIG. 11.

In some embodiments, the analytics-platform computing system 12 is acomputing system having a plurality of monitoring computing instances26, each of which may be a different physical computing device operatingan operating system on one or more processors connected to memory, forexample operating in a single memory address space. Or the monitoringcomputing instances 26 may be virtual machines, e.g., virtual machinesexecuted by a virtualization host, and several virtual machines may behosted on a single physical computing device, or some instances may hosta single virtual machine on multiple physical computing devices. Ineither case, the computing devices may be one of the examples ofcomputing devices described below with reference to FIG. 13, such aslaptops, desktops, or rack-mounted computing devices, for example. Eachmonitoring computing instance 26 may have an operating system upon whichan application may be loaded and within which the application may beexecuted, and in some embodiments, some monitoring computing instances26 may include one or more physical and virtual machines.

In certain embodiments, the analytics platform computing system 12 maybe embodied as a cloud-based distributed application, such as anapplication deployed in a public cloud (e.g., the elastic compute cloudservice offered by Amazon.com, Inc. of Seattle, Wash.), or in a privatecloud operated as a virtualized infrastructure within an enterprise datacenter (for instance, based on the open-source KVM hypervisor). Someembodiments of the cloud-based analytics-platform computing system 12may scale (e.g., by adding or subtracting monitoring computing instances26) based on the computing load of the analytics-platform computingsystem 12. For example, scaling may be performed automatically basedsolely on the computing load or based on the computing load and otherfactors, such as the cost of marginal computing instances, bandwidth, orother resources, or scaling may be performed based solely (or partially)on one of these other factors, independent of load, or a combinationthereof, e.g., a subset. An analytics-platform computing system that isconfigured to scale based on load is expected to accommodate a variablenumber of monitored computing systems and monitored computing systems ofvariable size without incurring the cost of provisioning computingresources for the maximum expected load. Examples of such scaling aredescribed below with reference to FIG. 7. In other embodiments, theanalytics platform computing system 12 does not scale, does not scaleautomatically, or is not cloud-based and may be executed by a singlecomputing device, which is not to suggest that any other featuredescribed herein may not also be omitted in some embodiments.

The analytics-platform computing system 12, in some embodiments, may beoperable to monitor or manage computing systems 14, 16, and 18 formultiple users associated with client devices 20, 22, and 24 and thecomputing systems 14, 16, and 18, thereby providing computer systemmanagement software as a service (e.g., a subscription service). Asexplained in greater detail below, some embodiments may be capable ofassociating each of the different monitored computing systems 14, 16,and 18 with a different account, and in some embodiments, usersassociated with those accounts may monitor the corresponding one of thecomputing systems 14, 16, and 18 via one of the client devices 20, 22,or 24. In some embodiments described below, the client devices 20, 22,and 24 include a web browser (e.g., a laptop, desktop, smart phone, orremote virtual machine having a browser), and the analytics-platformcomputing system 12 is operable to serve a web-based interface to usersvia the web browser. Advantageously, some embodiments may provide acomputing system management service to each of a plurality of differentusers, each monitoring one of a plurality of different computingsystems, thereby potentially reducing or eliminating the need of suchusers to host or maintain their own computing system management program.Some embodiments, however, may have one analytics-platform computingsystem for each monitored computing system, and both systems may beoperated by the same entity, which is not to suggest that any otherfeature described herein may not also be omitted in some embodiments.

The monitored computing systems 14, 16, and 18 may each be a differentmonitored computing system associated with, and under the control of, adifferent entity, for example a different account of a cloud computingservice, a different operator of a data center, or a different user ofthe analytics-platform computing system 12. In some embodiments, some orall of the monitored computing systems 14, 16, and 18 may be executed onthe same cloud computing service or data center that executes theanalytics-platform computing system 12 or on different systems. Someembodiments of the monitored computing systems 14, 16, and 18 may behosted on examples of the above-mentioned public cloud, examples of theabove-mentioned private cloud, examples of the above-mentioned datacenters, or some combination thereof. In some instances, some or all ofthe computing systems 14, 16, or 18 may be executed on a virtualizedinfrastructure, such as a virtualized infrastructure executed within anenterprise data center. In some embodiments, one or more of themonitored computing systems may be characterized as a cluster computingsystem. Some embodiments may be operated on host devices under thecontrol of a single entity, or under the control of multiple entities,e.g., a volunteer distributed computing project.

In some embodiments, the computing systems 14, 16, and 18 may beexecuted, partially or substantially entirely, on a public cloudcomputing service. The cloud computing service may have certainattributes. For example, the physical infrastructure upon whichcomputing instances are built may be not visible to users of the cloudcomputing service. The cloud service provider may obscure from, orabstract away from, users details of the physical computing devices uponwhich the computing instances are hosted. Further, in some instances,users of the cloud computing service may have service-level agreementswith the provider of the cloud computing system that specify minimumperformance and uptime characteristics, and as described below, someembodiments of the analytics-platform computing system 12 may be capableof verifying whether these service-level agreements are met.

The computing systems 12, 14, 16, and 18, in some embodiments, may eachinclude a plurality of computing instances, such as the monitoringcomputing instances 26 of the analytics-platform computing system 12 orthe monitored computing instances 28 of each of the monitored computingsystems 14, 16, and 18. The computing instances 26 and 28 in each ofthese examples may be a physical computing device or may be a virtualmachine, either of which may execute an operating system and one or moreapplications performing certain tasks. The computing instances are notnecessarily physical computers, and in some embodiments, attributes andconfigurations of the physical computers upon which the computinginstances are executed may be obscured to those using the computinginstances and controlling the execution of the applications. Theapplications may be executing any of a wide variety of different tasks.For example, some applications may be executing a data analysisalgorithm, a database, a Web server, or any of a variety of other tasks.

In the course of executing these applications, the number of computinginstances may change. For example, some cloud computing systems areoperable to increase or decrease the number of computing instances basedon the computing load, for example based on the amount of data to beprocessed by the above-mentioned applications or the speed of suchprocessing, which in some use cases correlates with the number of usersinteracting with the services provided by the monitored computingsystems 14, 16, and 18. As described in greater detail below, theanalytics-platform computing system 12 may be capable of tracking newlyadded computing instances as those newly added computing instancesidentify themselves to the analytics-platform computing system 12.

Further, as the monitored computing systems 14, 16, and 18 execute theirapplications, attributes of the monitored computing instances may vary.Examples of such attributes include the amount of memory allocated to,or possessed by, each computing instance in use, the amount ofprocessing power of each computing instance in use (e.g., the percentageof time that a CPU is generally idle), attributes of network usage(e.g., input bandwidth use, output bandwidth use, or input or outputbandwidth use of certain types of traffic—for instance based on packetheaders, latency, packet loss, and the like), economic attributes (e.g.,the cost of instances, the cost of CPU cycles, the cost of memory, orthe cost of network traffic), and sensed physical properties of theunderlying computing device, such as temperature and vibrations. Asexplained in greater detail below, some or all of these attributes orsimilar attributes may be monitored by the analytics-platform computingsystem 12 such that a user operating one of the client devices 20, 22,or 24 can view information about such attributes of a computing system14, 16, or 18 under that user's control. (In some embodiments, one ormore of the client devices 20, 22, or 24 may be one of the monitoredcomputing instances 28, e.g., a virtual machine operating a web browserby which performance of that computing instance 28 and other computinginstances 28 is displayed.)

In some embodiments, some, all, or substantially all of the computinginstances 28 of a monitored computing system 14, 16, or 18 may execute acollector 30. As described in greater detail below with reference toFIG. 2, the collectors may be capable of introducing new computinginstances to be monitored to the analytics-platform computing system 12and initiating a monitoring session with the analytics-platformcomputing system 12 to monitor the new computing instance. Further, asdescribed in greater detail below with reference to FIGS. 5 and 6, thecollectors 30 may be capable of bundling, compressing, encrypting,buffering, and then pushing gathered data to the analytics-platformcomputing system 12 in a manner that is relatively robust tointerruptions in network connections between the collector 30 and theanalytics-platform computing system 12 and bursts of traffic over suchconnections. The collectors 30 may be executed within the operatingsystem of each of the monitored computing instances 28, for example as aparallel thread or process to those threads or processes executing theabove-described applications for each of the monitored computing systems14, 16, and 18.

In some embodiments, each monitored computing system 14, 16, and 18 mayperform a process for adding a new computing instance to the monitoredcomputing system, for example based on a load of the monitored computingsystem, for instance in response to the load exceeding a threshold, inresponse to a response time of some or all of the monitored computingsystem exceeding a threshold, or in response to one or more attributesof monitored computing instances exceeding some threshold or obtainingsome state. When adding a monitored computing instance, in someembodiments, a monitored computing system may request a new computinginstance from a cloud computing system provider or other resource forcomputing instances and transmit, or request that such a transmission beperformed, to the new computing instance a machine image including anoperating system and one or more applications to be executed within theoperating system, including the collector 30. Upon booting of this imageon the new computing instance to be monitored, as described in greaterdetail below with reference to FIG. 3, the collector 30 may initiatecommunication with the analytics-platform computing system 12, identifythe new computing instance to the analytics-platform computing system12, and then push data about the operation of the new computing instanceto the analytics platform computing system 12.

The illustrated embodiment includes three monitored computing systems14, 16, and 18, but other embodiments may include fewer or substantiallymore. In some embodiments, each monitored computing system 14, 16, and18 may be associated with an account, such as a subscription account,identified in memory accessible to the analytics-platform computingsystem 12. In some embodiments one (or one and only one) account may beassociated with each monitored computing system by theanalytics-platform computing system 12. In other embodiments, oneaccount may be associated with one or more monitored computing systems,and each such monitored computing system may be associated with a systemidentifier also associated with the account that distinguishes among thevarious monitored computing systems of the account. As explained ingreater detail below with reference to FIG. 10, users associated withsuch accounts may receive data indicative of the operation ofcorresponding monitored computing systems through one of the clientdevices 20, 22, and 24 by identifying the account to theanalytics-platform computing system 12, for instance by entering anaccount identifier and a password in a web user interface.

The client devices 20, 22, and 24 may be a variety of different types ofcomputing devices, including the above-described computing instances,and the devices described below with reference to FIG. 13, such aspersonal computers, laptops, smart phones, or other devices having auser interface capable of presenting data about the operation of amonitored computing system. In some embodiments, some or all of theclient devices 20, 22, and 24 may not have such a interface, for examplesome of the client devices 20, 22, and 24 may be a server or othercomputing device capable of controlling one of the monitored computingsystems 14, 16, or 18 based on data from the analytics-platformcomputing system 12. For instance, the client devices 20, 22, or 24 mayadd computing instances to a monitored computing system based on dataindicating a load on the monitored computing system has increased, hasincreased above a threshold, has increased at a rate such that the rateexceeds a threshold, or data indicating a response time of a monitoredcomputing system has increased or increased above a threshold.Similarly, such embodiments may terminate computing instances from amonitored computing system upon a decrease in such factors, e.g., adecrease below similar thresholds.

The network 25 may include a variety of different types of networks,either individually or in combination. In some embodiments, the network25 may include the Internet. In another example, the network 25 mayinclude a wide area network or a local area network, such as anEthernet. The network 25 may span a relatively large geographic area, insome embodiments. For example, the analytic-platform computing system 12may be remote from the monitored computing systems 14, 16, and 18, whichmay be each remote from one another, and the systems 12, 14, 16, and 18may all be remote from the client devices 20, 22, and 24, which each mayalso be remote from one another, for example these components may befurther than 10 miles apart, further than 100 miles apart, or furtherthan 500 miles apart.

Like the other features and embodiments of other figures describedherein, embodiments are not limited to systems having the same number offeatures as those illustrated in FIG. 1. For example, other embodimentsmay include multiple analytics-platform computing systems 12, a singleor many more monitoring computing instances 26, a single or many moremonitored computing systems 14, 16, and 18, a single or many moremonitored computing instances 28 within each of the monitored computingsystems 14, 16, and 18, more than one collector 30 within each monitoredcomputing instance 28, and zero, one, or many more than one clientdevices 20, 22, and 24 each associated with one of the monitoredcomputing system 14, 16, and 18. This is not to suggest that any otherembodiment described herein is limited to the exact number of featuresillustrated in a figure.

FIG. 2 illustrates an embodiment of the collector 30 described abovewith reference to FIG. 1. The same collector 30 may be executed in eachof the above-described monitored computing instances 28, or in someembodiments, different collectors may be configured for differentcomputing instances 28. The collector 30 may be operated in combinationwith the other components described above with reference to FIG. 1, orthe collector 30 may be used to collect data in other computer systems,such as networking systems or storage systems, for other computer systemmanagement programs.

As described in greater detail below, in some embodiments, the collector30 may be capable of identifying a new computing instance to theanalytics-platform computing system 12, which may lower labor costs andreduce response time associated with configuring the analytics-platformcomputing system 12 to monitor a new computing instance relative tosystems in which the analytics-platform computing system 12 initiatescommunication or polls data from the computing instance. Further, as isalso described in greater detail below, the collector 30 may be capableof compressing gathered data in a manner that tends to reduce overheadassociated with transmission of the data to the analytics-platformcomputing system 12. Embodiments of collectors 30 are also capable ofbuffering and modulating the transmission of the gathered data such thatdata is retained in the event of a network failure, or failure of anyother component existing in-between the collector and functioningmonitoring computing instance 26 including a component or process of theanalytics-platform itself, and such that surges in the transmission ofdata are mitigated following recovery of the network 25 after such afailure. The collector 30 may also be capable of receiving updates ofcollector software from the analytics-platform computing system 12,thereby potentially lowering the burden on users of monitored computingsystems desiring to keep collector software up-to-date.

In some embodiments, the collector 30 includes an operating systeminterface 32, an input/output module 34, a data acquisition module 36, asession initiator module 38, a collector updater module 40, and acollector controller module 42. These modules are described and depictedas separate functional blocks; however hardware or software implementingthe corresponding functions may be intermingled, conjoined, separated,or otherwise organized relative to the functional blocks describedherein.

The collector 30, in some embodiments, may be capable of collecting ormeasuring performance, configuration, and resource utilization data(referred to as metrics) from the operating system executing on themonitored computing instance via the operating system interface 32. Themetrics may be gathered by the data acquisition module 36 and may bereferred to as metrics of the monitored computing instance. The metricsmay be indicative of performance, resource utilization, componenthardware and software component identities and versions, costs of use,and other attributes. The resulting metric data, in some embodiments,may be pre-processed by the input/output module 34 by packaging the datainto time-based buckets or other batches aggregated according to othercriteria, for example based on a predetermined quantum of data, therebypotentially reducing the amount of data to be transmitted to theanalytics-platform computing system 12 and reducing operating costs andnetwork usage. Other embodiments, however, may not pre-process the data,which is not to suggest that any other feature described herein may notalso be omitted in some embodiments. In this embodiment, the operatingsystem interface 32 may be capable of making calls to an applicationprogramming interface of the operating system of the monitored computinginstance, for example in response to requests for data or commands fromthe other components of the collector 30.

In some embodiments, the input/output module 34 is capable ofcommunicating with the other components of the collector 30 and with theanalytics-platform computing system 12 via the network 25 (FIG. 1). Asillustrated by FIG. 2, this embodiment of an input/output module 34includes a throttle module 44, a buffer module 46, an encryption module48, and a compression module 50. Other embodiments may includeadditional modules or fewer modules, again which is not to suggest thatother features may not also be omitted.

FIG. 2 illustrates some of these modules as being spatially interspersedbetween other modules, but FIG. 2 is not limited to a particulartopology, and the components of FIG. 2, as is the case with the otherblock diagrams herein, may communicate with one another, in some usecases and some embodiments bi-directionally, either directly orindirectly through other modules or components. Such communication mayoccur through a variety of techniques at a variety of different layersof abstraction, including via a wired or wireless network, via a buswithin a computing device, by way of calling module or componentapplication program interfaces (APIs), or via reference to value storedin memory, such as values associated with variables within a program, orvia copies of such values passed between processes or sub-programs.

The input/output module 34 and its components 44, 46, 48, and 50 may beoperable to execute portions of the processes described below withreference to FIG. 3 and FIG. 4 and the processes described below withreference to FIG. 5 and FIG. 6, in some embodiments. As explained ingreater detail below with reference to these figures, the throttlemodule 44 may be capable of throttling the output of the collector 30 tothe analytics platform computing system 12 such that sudden spikes innetwork traffic to the analytics-platform computing system 12, forinstance following a systemic failure or recovery from a networkfailure, are mitigated, thereby potentially reducing the likelihood of aspike in traffic from one monitored computing system impeding the flowof data from another monitored computing system. The buffer module 46may be capable of storing (e.g., buffering) metrics such that datalosses are avoided or mitigated when the throttle module 44 (or anetwork outage) causes the input/output module 34 to transmit data at aslower rate than the collector 30 is gathering data. The encryptionmodule 48 may be operative to encrypt data from the collector 30, suchthat an entity monitoring network traffic, for example an entityperforming deep packet inspection of traffic to the analytics-platformcomputing system 12, may be impeded from inferring details about theoperation of a monitored computing system 14, thereby potentiallysatisfying some regulatory requirements for the security of datarelating to certain systems and potentially limiting the likelihood ofcertain types of attacks on system security, such as attacks based onchanges in resource usage in response to more or fewer characters of apassword being correct. The compression module 50 of this embodiment maybe operative to reduce the amount of network traffic used to convey agiven amount of information from the collector 30 to theanalytics-platform computing system 12. Examples of compression aredescribed below with reference to FIG. 5.

In this embodiment, the data acquisition module 36 includes an operatingsystem status interface module 52, a network-usage interface module 54,a sensor interface module 56, a data pre-processor module 58, and a dataaggregator module 60. Other embodiments may include additional modulesor fewer modules, again which is not suggest that other features may notalso be omitted or supplemented.

In some embodiments, the operating system status interface module 52,the network usage interface module 54, and the sensor interface module56 may be capable of gathering metrics about the monitored computinginstance. For example, the operating system status interface module 52may be capable of commanding the operating system, via the operatingsystem interface 32, to return data indicative of resource utilization,configuration, and performance of the operating system, resources of theoperating system, or software executed in the operating system,including resource utilization and performance of applications and otherprocesses. Examples of such metrics include utilization of systemmemory, for instance utilization of random-access memory, utilization ofvarious other types of memory, such as cache memory, persistent storagememory (e.g., hard disk drive memory, solid-state drive memory, and thelike), graphics memory, and other forms of special-purpose memory, suchas buffer memory in a network interface card. In another example, themetrics may include utilization of various types of processors, such asutilization of one or more cores of a central processing unit, andutilization of a graphics processing unit, for example. Utilization maybe expressed in a variety of formats, for example a percentage of acapacity (such as in comparison to historic averages, peaks and troughswhere the historic data was previously recorded by the analyticsplatform computing system, in comparison to historic data gathered froma wide variety of time and date ranges, in comparison to aggregatehistoric data previously gathered from similar or different instances,running in the same or different cloud/data center/virtualinfrastructure), an absolute amount of utilization, for instance inmegabytes or cycles of a CPU, or a binary indicator of whether somecondition has been obtained or not been obtained. Metrics may includedata logged by the operating system, including error conditions, anddata indicative of which processes are running Metrics may also includeperformance metrics, for example data indicative of the amount of timevarious tasks take, such as the time taken to retrieve data from memoryor write data to memory, or time taken to perform certain processingtasks, such as the time taken to iterate a portion of an application ortime taken to yield some results. Other metrics may include metrics thatare application or process specific, such as the above-described metricsthat are attributable to a given process or application, and a list ofsuch processes or applications. Some embodiments may be capable ofobtaining metrics indicative of the configuration of the monitoredcomputing instance, for example a size of a memory space of themonitored computing instance, for instance whether the monitoredcomputing instance is a 32-bit or 64-bit system, system informationabout allocated or present processing power and memory, and the like.Gathered data may also include data indicative of versions ofapplications, drivers, and firmware. Metrics may also include cost dataassociated with the operation of the computing instance, for instancecost data associated with electrical power, cost data of units ofprocessing, costs data of units of memory, and cost data of networktransmissions or reception of data.

In some embodiments, the network-usage interface module 54 may becapable of obtaining information relating to network usage via theoperating system interface 32 by transmitting commands to the operatingsystem interface 32 and receiving data retrieved via the operatingsystem interface 32. Examples of network usage data include dataindicative of a rate or amount of network traffic received by ortransmitted by the monitored computing instance and data indicative ofperformance of network traffic, such as packet loss, latency, bandwidth,routes, and data indicative of recipients of network traffic ortransmitters of network traffic to the monitored computing instance. Thedata indicative of network traffic may also include data that isspecific to particular types of network traffic, for example networktraffic encoded according to particular protocols, data particular tocertain applications, data particular to network traffic receivedthrough or transmitted through a particular port, and data indicative ofnetwork traffic received from or transmitted to some other computingdevice. The data indicative of network traffic may also include dataindicative of the operation of a network interface card, physical orvirtual, such as data indicative of an amount of data stored in a bufferof the network interface card and data indicative of the capabilities ofthe network interface card, such as supported protocols, an amount ofmemory, supported features, and firmware versions. In some embodiments,the network usage interface module 54 is also operable to gather dataindicative of information encoded in network traffic, such as dataavailable through deep packet inspection of network traffic, from whichcan be derived transaction information including transaction responsetimes, for example the response times for various application or storageprotocol transactions.

In some embodiments, the sensor interface module 56 is operable toobtain data from various sensors of the computing device providing themonitored computing instance by transmitting requests for such data tothe operating system interface 32 and receiving results retrieved by theoperating system interface 32 from sensors. Examples of such datainclude temperature data indicative of the temperature of variouscomponents of the physical computer provided by the monitored computinginstance, such as the temperature of a processor (e.g. a centralprocessing unit, a digital signal processor, a graphics processing unit,a memory controller, a hard disk drive controller, and the like), thetemperature of memory (e.g., random-access memory, cache memory, or ahard disk drive memory, such as a solid-state drive), the temperature ofa power supply, or (i.e., and/or) the ambient temperature within a caseor rack in which the monitored computing instance is disposed. Otherexamples of sensor data may include audio data or motion sensor dataindicative of vibration of components of the physical computer providingthe monitored computing instance (e.g., capacitor or fan vibrations) ora current draw or a voltage of various components, such as fans,processors, memory, or a power supply. In some embodiments, obtainingsensor data may include accessing some form of clock chip or othercomponent that provides, or can be made to provide signals orindications on a regular basis, either absolutely or relative to the‘virtual clock’ of virtual machines.

The metrics gathered by the interface modules 52, 54, and 56 may beprocessed by the data pre-processor module 58, in some embodiments. Inembodiments having a data pre-processor module 58, this module mayperform certain analyses on the gathered data to identify certainmetrics that are discernible within the subsequently described batchesof data formed by the data aggregator 60. For instance, the datapre-processor 58 may be capable of identifying within data associatedwith these batches a maximum value, a minimum value, an average value, amedian value, a standard deviation, a variance, a count of some events,and the like. The data pre-processor 58 may also be capable of reducingthe granularity of metrics, for example by sampling the data obtained bythe module 52, 54, and 56.

The data aggregator module 60, in this embodiment, may be capable ofreceiving metrics from the data pre-processor 58 or directly from theinterfaces 52, 54, and 56 and packaging the metrics in batches. Thebatches may be defined based on time, for example data arriving within aduration, such as a predetermined or dynamically determined duration oftime that remains constant (e.g., a period) or varies during theoperation of the collector 30. In another example, the batches may bedefined based on an amount of data, for example each batch may contain apredefined or dynamically determined amount of data, such as onekilobyte, 10 kilobytes, or 1 megabyte, for instance. In another example,the batches may be defined based on the occurrence of events, forexample a batch may begin when a process executed by the monitoredcomputing instance starts and end when the process ends. Batching thedata is expected to reduce the amount of data transmitted to theanalytics-platform computing system 12 while still providing dataindicative of the operation of the monitored computing instance over thebatching duration. In some embodiments, the batches may be relativelysmall in order to provide a relatively high resolution view of theoperation of the monitored computing instance, for example the batchesmay span an amount of time less than or approximately equal to 30seconds, 20 seconds, 10 seconds, 5 seconds, one second, or 100microseconds or less. Other embodiments, however, may not batch data,and some or all of the gathered data may be transmitted to the analyticsplatform computing system 12, which is not to suggest that any otherfeature described herein may not also be omitted in some embodiments.

In some embodiments, the data aggregator module 60 may include an input,a buffer, a batch manager, and an output. The input may receive datafrom the data pre-processor module 58 and store the data in the buffer.The batch manager may determine when a batch is complete and, inresponse, instruct the output to transmit the batch to the input/outputmodule 34 and clear the buffer.

As noted above, the controller 30 may also include the session initiatormodule 38, in some embodiments, which may include an instance identifiergenerator 62 and an account identifier module 64. Details of theoperation of the session initiator module 38 are described in greaterdetail below with reference to FIG. 3. The session initiator module 38may be capable of requesting identifiers from these modules 62 and 64and initiating a monitoring session with the analytics-platformcomputing system 12.

In some embodiments, the session initiator 38 is capable of initiatingcommunication with the analytics-platform computing system 12, withoutthe analytics-platform computing system 12 first communicating with thecollector 30 or the new monitored computing instance. In someembodiments, the session initiator 38 is capable of alerting theanalytics-platform computing system 12 to the existence of a newcomputing instance to be monitored without the analytics-platformcomputing system 12 otherwise receiving instructions indicating theexistence. The session initiator 38 may be characterized as beingcapable of self identifying the collector 30 or the monitored computinginstance to the analytics-platform computing system 12. The sessioninitiator module 38 is expected to simplify the burden associated withconfiguring an analytics-platform computing system 12 to monitor acomputing system by automatically informing the analytics-platformcomputing system 12 of which computing instances are to be monitored.However, other embodiments may not include a session initiator module38, and some embodiments may include an analytics-platform computingsystem 12 that is configured to identify a new monitored computinginstance based on signals received from some other source, for examplesignals received from one of client devices 20, 22, or 24 or one of theother monitored computing instances 28 tasked with requesting a newcomputing instances from a cloud service provider, which again is not tosuggest that any other feature herein is required in all instances.

The instance identifier generator module 62 may be capable of forming anidentifier, such as an identification number, code, or other string,that is unique to (or likely to be unique to, for example more likelythan one in 100,000) each monitored computing instance within amonitored computing system or each monitored computing instance.Further, in some embodiments, the instance identifier generator module62 is capable of forming such an identifier without receivinginformation from the analytics-platform computing system 12, for exampleprior to initiating contact with the analytics-platform computing system12. The instance identifier may be formed based on a variety ofattributes of the monitored computing instance, for example someoperating systems alone, or by way of interaction with another componentmay provide a unique identifier which may be used, a network address ofthe monitored computing instance, a MAC address of the monitoredcomputing instance, serial numbers of components of the monitoredcomputing instance, or attributes likely to vary, such as a pseudorandomnumber generated by the monitored computing instance, less significantdigits of a temperature of the monitored computing instance, and lesssignificant digits of a voltage measured by the monitored computinginstance. In some embodiments, these values may be inputs to a hashfunction that generates the instance identifier.

Drawing on these sources of values that are likely to vary among themonitored computing instances is expected to yield instance identifiersthat are likely to be unique among the monitored computing instances,thereby potentially providing an identifier with which the collector 30may initiate a session with the analytics-platform computing system 12without the analytics-platform computing system 12 centrallycoordinating the allocation of instance identifiers, and potentiallyrelieving users of the burden of configuring the analytics-platformcomputing system 12 for such central coordination. In other embodiments,however, the instance identifier may be received from some other source,for example from a client device 20, 22, or 24 or another computinginstance coordinating the operation of other computing instances or fromthe analytics-platform computing system 12, which is not to suggest thatother features cannot also be omitted in some embodiments.

Similarly, the account identifier module 64 may obtain an identifierthat is unique to (or likely to be unique to) an account associated withthe monitored computing system of the monitored computing instance. Theaccount identifier, in some embodiments, may be obtained from acomputing instance controlling the instantiation and termination of newcomputing instances of a monitored computing system, for example. Otherembodiments may not include an account identifier, for instance, someembodiments may include an identifier for a monitored computing systemthat is not associated with an account.

The session initiator module 38 may also include an address of theanalytics-platform computing system 12, for example an address reachablethrough the network 25 (FIG. 1). The address may take a variety offorms, for example the address may be an Internet protocol address, suchas an Internet protocol version 4 or version 6 address, or the addressmay be a uniform resource identifier associated with the network addressof the analytics-platform computing system 12 and resolvable through adomain name service. The session initiator 38 may also be operative toestablish a secure connection with the analytics-platform computingsystem 12, for example by exchanging encryption keys.

The collector updater module 40 may be capable of determining theversion or configuration of the collector 30, requesting data indicativeof newer versions or a newest version of a collector from theanalytics-platform computing system 12, determining based on this datawhether to upgrade the collector 30, requesting data encodinginstructions for a new collector corresponding to the newer version ornewest version from the analytics-platform computing system 12, andlaunching a module configured to uninstall the old version of thecollector 30 and install the new version or newer version. In someembodiments the determination to upgrade may be made at theanalytics-platform computing system 12 or in some other computing systemor device.

The updater module 40 may, in some embodiments, receive a signal fromthe session initiator module 38 indicating that a new monitoring sessionhas been established with the analytics-platform computing system 12,and in response, the collector updater 40 may perform the stepsdescribed above to determine whether to upgrade. In some embodiments,the collector updater module 40 may perform a similar determinationrepeatedly during the operation of the collector 30, for example uponthe hour, once a day, once a week, or once a month. The collectorupdater module 40 may be capable of updating the collector 30 to a newversion during the operation of a monitored computing instance withoutlosing data measured by the monitored computing instance, or with losingrelatively little data monitored by the collector 30. For example, thecollector updater 40 may be capable of installing a new collectorembodying the new version while the collector 30 continue to operate,determining that the new collector is operative and has established amonitoring session, instructing the older version of the collector 30 tostop gathering data, determining that the remaining data stored in thebuffers of the older version of the collector 30 have been transmitted,and then terminating the older version of the collector 30.

The collector controller 42 may be capable of coordinating the operationof the components of the input-output module 34, the data acquisitionmodule 36, the session initiator module 38, the collector updater module40, and the operating system interface module 32. For example, thecollector controller 42 may instantiate and terminate each of thesemodules 34, 36, 38, 40, and 32, and in some embodiments, these modulesmay bi-directionally communicate with one another via the collectorcontroller module 42, for instance by passing values by reference or ascopies of values as parameters returned to the collector controller 42,which may then pass these values or references to other modules. In someembodiments, the collector controller 42 may be executed in response toa new computing instance booting or a new version of the collector 30being installed, and upon (in response to) being executed, the collectorcontroller module 42 may launch the session initiator module 38 toestablish a monitoring session with the analytics-platform computingsystem 12, then launch the update module 40 to determine whether thecollector 30 is the correct version, then upon determining that thecollector 30 is the correct version, launch the data acquisition module36 and the input/output module 34 to begin gathering and reporting datato the analytics-platform computing system 12.

The collector 30, in some embodiments, is expected to automaticallyreconfigure the analytics-platform computing system 12 to monitor newcomputing instances as new computing instances are added to a monitoredcomputing system and automatically update the collector as new versionsare promulgated. These techniques, either individually or in isolation,are expected to reduce the burden on those attempting to monitorcomputing systems, particularly those attempting to monitor scalablecomputing systems formed within a cloud computing service that supportsautomatic provisioning of additional computing resources based on loador other needs. These techniques may be prohibited in specific use casesfor a variety of reasons, such as security concerns. The collector 30 insome embodiments may have the automated reconfiguration and automatedupdate capabilities permanently disabled. In such embodiments,reconfiguration and collector updates may be carried out by manualintervention. Other embodiments, however, may not necessarily providethese advantages, and various engineering trade-offs may be made to usethe techniques described herein to obtain other objectives.

FIG. 3 illustrates an embodiment of a process 66 for initiating amonitoring session, for instance with the analytics-platform computingsystem 12, upon the launch of a new computing instance. Some, all, orsubstantially all of the process 66 may be performed by the sessioninitiator module 38, for instance in cooperation with the othercomponents of the collector 30 of FIGS. 1 and 2. Applications of theprocess 66, however, are not limited to these configurations.

The process 66 begins with operating a monitored computing system, asindicated by block 68. Operating a monitored computing system mayinclude operating one or more monitored computing instances of themonitored computing system. In some embodiments, the instances may beformed by uploading from a main instance, or a controlling clientdevice, a machine image including an operating system, theabove-described collector, and applications to be executed by theinstance to perform the tasks that the computing system is intended toperform for a user. New instances may be obtained, in some embodiments,by transmitting a request for a new instance to a cloud service provideror other system for dynamically allocating computing resources, such asan elastic data center or virtualized computing infrastructure provider.The request may include specifications of the requested computinginstance, for example an amount of addressable memory supported,processor specifications such as 32 bits or 64 bits, memoryspecifications and the like. Some requests may also specify an operatingsystem.

Next, in some embodiments, the process 66 includes determining whether anew computing instance has launched, as indicated by block 70. In someembodiments, this and the subsequent steps may be performed by thecollector 30, which may be launched upon the boot of the new computinginstance, thereby determining that the new computing instance haslaunched. In other embodiments, software or hardware external to the newcomputing instance may determine that a new computing instance haslaunched. For example, a computing device that requests the launch ofthe new computing instance may make this determination upon having madethe request or upon having received confirmation that the request wassatisfied. Upon determining that a new computing instance has notlaunched, in response, the process 66 may return to block 68.Alternatively, upon determining that a new computing instance haslaunched, in response, the process 66 may proceed to the next stepdescribed.

Next, in some embodiments of process 66, an instance identifier of thenew computing instance may be obtained, as indicated by block 72.Obtaining an instance identifier may be performed with the instanceidentifier generator module 62 described above with reference to FIG. 2.In some embodiments, the instance identifier may be a number, code, orother string that is unique or likely to be unique to the new computinginstance, and in some embodiments, the new instance identifier may beobtained based on attributes of the new computing instance, such thatthe instance identifier is formed without central coordination from, forexample, an analytics platform.

Next, in some embodiments of process 66, an account identifier of anaccount associated with the computing system of the new computinginstance may be obtained, as indicated by block 74. This step may beperformed with the above-described account identifier module 64 of FIG.2. The process 66 also includes obtaining an address of an analyticsplatform, as indicated by block 76, which may include the abovedescribed techniques for obtaining an Internet protocol address or auniform resource identifier. In some embodiments, the address may beobtained by recalling the address from memory allocated to a collector,and the address may be encoded as a constant in code executed as thecollector. In some embodiments, each collector of each monitoredcomputing instance of each monitored computing system may obtain thesame address.

The process 66 in some embodiments includes initiating a session withthe analytics platform by transmitting a request to monitor thecomputing instance to the obtained address, as indicated by block 78.Initiating a session may include transmitting a signal indicative of theexistence of a new computing instance to be monitored to the analyticsplatform. In some embodiments, the signal indicative of the new instancemay constitute a request. In certain embodiments, the firstcommunication between the analytics platform and the new computinginstance may be a transmission by the collector or other transmissionsfrom the new computing instance to the analytics platform. Initiatingcommunication from the new computing instance is expected to simplifyconfiguration of the analytics platform, as the analytics platform, insome embodiments, may not need to be reconfigured manually for each newcomputing instance, though not all embodiments necessarily provide thisbenefit. The initiated session, in some embodiments, may includetransmissions from a monitored computing instance to the analyticsplatform and transmissions from the analytics platform to the monitoredcomputing instance. As explained in greater detail below, data receivedat the analytics platform may be associated with the session, and thesession may be associated with the monitored computing instance, forexample with the identifier of the new computing instance, such thatsession data received at the analytics platform may be associated withthe monitored computing instance and, in some embodiments, the accountidentifier.

The process 66 also includes, in this embodiment, transmitting theinstance identifier and the account identifier to the analytics platformfor association with the session, as indicated by block 80. In someembodiments, this transmission may be a transmission by which a sessionis initiated, as described above with reference to block 78. In otherembodiments, the session may be initiated, and the identifier is may betransmitted subsequently, for example by the collector controller eitherin response to confirmation from the analytics-platform computing system12 that the session has been established or in response to a request forthe identifiers from the analytics-platform computing system 12.

Embodiments of the process 66, like the other processes describedherein, are not limited to the particular sequence illustrated in thefigure. For example, in some embodiments, account identifiers andinstance identifiers may be obtained after initiating a session.Further, like the other systems, devices, and processes describedherein, not all embodiments necessarily include all the features ofprocess 66, for instance some embodiments may omit certain steps orinclude additional steps.

FIG. 4 illustrates an embodiment of a process 82 for reporting data froma monitored computing instance. The process 82 may be performed by thecollector 30 described above with reference to FIG. 2, thoughembodiments are not limited to the variations of the collector 30described above. As described in greater detail below, the process 82may convey data from the monitored computing instance to the analyticsplatform in a fashion that is relatively easy for users to configure, isrelatively robust to interruptions in network communication, and isrelatively parsimonious with bandwidth, while providing relatively highresolution indicators of the performance of a monitored computinginstance.

The illustrated process 82, in some embodiments, begins with initiatinga session between a computing instance of a monitored computing systemand an analytics platform, as indicated by block 84. This step, in someembodiments, may be performed by the above-described session initiator88 of FIG. 2 by executing the process 66 of FIG. 3. In some embodiments,the session is initiated by the monitored computing instance, and inother embodiments, the session is initiated by the analytics platform orby some other computing device.

Next, in some embodiments, the process 82 includes updating a collectorof the monitored computing instance, as indicated by block 86. Updatingthe collector may be performed by the above-described collector updatermodule 40 of FIG. 2 using the techniques described with reference to theoperation of this module 40.

The process 82, in some embodiments, also includes obtaining collectorparameters, as illustrated by block 88. Obtaining collector parametersmay include obtaining user configurable parameters that control theoperation of the collector. Examples of user configurable parametersinclude selections by a user of the monitored computing system (forinstance a user who controls or builds the monitored computing system inorder to serve customers of the user) regarding which data istransmitted from the monitored computing instance, how the data ispre-processed and processed, and how the data is identified and grouped.For instance, the collector parameters may include a parameter thatspecifies how data is to be batched, for example the duration of asubsequently described aggregation period, such as the above-describedtime-based batches of metrics.

Other examples include data indicative of which metrics are to betransmitted to the analytics platform and the format for thosetransmissions. For instance, some embodiments may specify that differentcategories of metrics be transmitted in a particular sequence, such thatthe categories of the metrics can be identified at theanalytics-platform computing system 12 based on the sequence withoutalso transmitting labels for the categories, thereby potentiallyreducing the amount of data exchanged between the collector and theanalytics platform. By way of example, the collector parameters mayspecify that a processor usage metric is transmitted first, followed bya delimiter, such as a comma, followed by a memory usage metric, then adelimiter, followed by a network usage metric, and so on. The collectorparameters, including sequences for data transmission, may be obtainedfrom the analytics-platform computing system 12, which may retrieve thecollector parameters based on an account identifier received upon theinitiation of a session in step 84 and may transmit the collectorparameters to the collector. Establishing such a sequence based oncollector parameters is expected to reduce network usage relative tosystems that transmit parameters using various markup languages, such asextensible markup language (XML) or JavaScript object notation (JSON).In other embodiments, the transmitted data may be labeled with eachtransmission, and this benefit may not be provided.

Next, in some embodiments of process 82, metrics of the computinginstance may be obtained, as indicated by block 90. Obtaining metricsmay be performed with the above-described data acquisition module 36using the techniques described with reference to the operation of thatmodule. In particular, some embodiments may obtain metrics with theabove-described interface modules 52, 54, and 56 by communicating withthe operating system interface 32.

Some embodiments of the process 82 include determining whether anaggregation period has elapsed, as illustrated by decision block 92. Theaggregation period may be a period of time within which obtained data ispackaged or otherwise grouped into time-based buckets or other batches.The duration of the aggregation period may be one of the obtainedcollector parameters obtained in step 88. In some embodiments, theduration may be one of the durations described above with reference tothe data aggregator 60. The duration may be selected based on trade-offsbetween the amount of data to be conveyed between the analytics platformand the monitored computing instance and the desired resolution ofanalyses performed by the analytics platform, as described below.

Upon determining that the aggregation period has not elapsed, inresponse, the process 82 may return to block 90. Alternatively, upondetermining that the aggregation period has elapsed, in response, theprocess 82 may proceed to block 94.

As illustrated by block 94, the process 82 in some embodiments includesforming a metric data batch indicative of metrics obtained during theaggregation. Forming a metric data batch may include the steps describedabove with reference to the operation of the data pre-processor module58 and the data aggregator module 60 of FIG. 2. In some embodiments,forming metric data batches includes calculating various statistics suchas maximum values, minimum values, median values, average values,counts, or binary alarms, and the like. Forming a metric data batch mayalso include sequencing the data according to the sequence obtained withthe collector parameters, including inserting delimiters between datavalues, as described above with reference to step 88. Alternatively oradditionally, some embodiments may include encoding the data in a markuplanguage, such as XML or JSON, for instance, encoding the data in ahierarchical tree data structure having metadata descriptive of nodes ofthe tree.

Next, in the present embodiment of process 82, the formed metric databatch may be output to the analytics platform, as indicated by block 96.Outputting the data may include outputting the data with theabove-described input/output module 34 of FIG. 2 using the techniquesdescribed above with reference to the operation of this module. In someembodiments, the data is output with the process described below withreference to FIGS. 5 and 6. Other embodiments, however, may output thedata without performing some or all of the steps of FIGS. 5 and 6, whichis not to suggest that other features described herein may not also beomitted, and some embodiments may perform the process 82 in a differentorder from the steps depicted, without including some of the stepsdepicted, or by including additional steps, as is the case with theother processes described herein.

FIGS. 5 and 6 illustrate processes 98 and 100 for outputting data from amonitored computing instance to an analytics platform. In someembodiments, the processes 98 and 100 may be performed duringoverlapping time periods, for example concurrently by different threadsor processes of the monitored computing instance. As explained ingreater detail below, the concurrent operation may facilitate bufferingof data such that the processes 98 and 100 are robust to interruptionsin network traffic, mitigating data loss during such interruption, andmitigating surges of data following restoration of service after aninterruption or other source of spikes in data to be transmitted. Otherembodiments, however, may perform the processes 98 and 100non-concurrently, for example sequentially.

The process 98, in some embodiments, begins with obtaining a metric databatch, as indicated by block 102. Obtaining a metric data batch mayinclude obtaining a metric data batch through the steps up to andincluding the step 94 of process 82 described above. The obtained metricdata batch may include a batch of data obtained over some time period,such as over an approximately or exactly 0.5 second, 1 second, 5 second,20 second, or 5 minute or less window of time.

In some embodiments, the process 98 includes compressing the metric databatch, as illustrated by block 104. The data may be compressed with avariety of techniques, for example using the above-described compressionmodule 50 of FIG. 2. In some embodiments, the data may be compressed byidentifying patterns existing within the data, such as a long string ofrepeating characters, associating the pattern with a shorter string,replacing the pattern with the shorter string, and outputting theresult. For instance, a string of zeros may be replaced with a stringthat identifies the character zero and the number of zeros. Similartechniques may be used for other repeating patterns, such as repeatingpatterns of zeros and ones.

In some embodiments, the process 98 includes encrypting the compressedmetric data batch, as illustrated by block 106, and which may beperformed in some embodiments by the above-described encryption module48 of FIG. 2. Encrypting the compressed metric data batch may includeencrypting the data based on an encryption key obtained during theabove-described process for initiating a session between a computinginstance and an analytics platform. Encryption may also include saltingthe data with a random number of leading or trailing values to impedeefforts to measure an amount of data being transmitted. Encryption, likemany of the other steps described herein, may be performed at adifferent part of the process 98 or the process 100. For example,encryption may be performed on a group of metric data batches retrievedfrom a buffer during the process 100, as described in greater detailbelow. Encrypting a larger collection of such data is expected to resultin greater obfuscation of the encrypted data.

Next, some embodiments of the process 98 may store the encrypted metricdata batch in a buffer. The buffer may be, or may be controlled by, thebuffer module 46 described above with reference to FIG. 2. In someembodiments, the buffer is a first-in first-out buffer, for example aring buffer having memory for storing data, memory for storing an inputpointer value that is incremented through addresses of the ring buffereach time a new value is written to one of the addresses of the ringbuffer, and memory for storing an output pointer value that isincremented through addresses of the ring buffer each time a value isread from one of the addresses of the ring buffer. Embodiments having aring buffer may also include an input counter for incrementing the inputpointer and an output counter for incrementing the output pointer. Aring buffer is expected to occupy a predetermined amount of memory ofthe computing instance, potentially preventing the collector fromcausing a memory error by consuming excess memory of the computinginstance. Other embodiments, however, may not use a ring buffer. Forexample, some embodiments may consume additional memory as additionaldata is buffered. In other embodiments, the buffer is a last-infirst-out buffer. The selection between these types of buffers maydepend upon whether a user prefers more up-to-date data to be deliveredfirst or whether the data arrive in the sequence with which it wasacquired. The buffer is expected to store data during periods in whichdata is acquired faster than it can be transmitted, for example duringperiods in which network traffic is slow, during periods in which theanalytics-platform computing system 12 is overloaded, or during periodsin which the acquisition of data surges, for example when the computinginstance being monitored has a systemic error. Other embodiments,however, may not include a buffer, and data may be transmitted as it isacquired, which is not to suggest that any other feature may not also beomitted in some embodiments.

The buffer data may be transmitted by executing the process 100 of FIG.6. In some embodiments, the process 100 begins with retrieving encryptedmetric data batches from the buffer, as illustrated by block 110. Asingle batch may be obtained, a portion of a single batch may beobtained, or multiple batches may be obtained from the buffer perretrieval request. As described above, the obtained batches may be thelast batches input into the buffer or the oldest batches in the buffer,or the batches may be prioritized with some other technique, for examplebased on the content of the data within the batch.

Some embodiments of the process 100 include determining whether alatency of transmissions to the analytics platform (which may includetime taken for the platform to process receipt of the data) is greaterthan a threshold, as illustrated by block 112. This determination may beperformed by the above-described throttle module 44 of FIG. 2. Highlatency is expected to be indicative of surges in network traffic,issues with the transmission of data across the network, or theanalytics-platform computing system 12 being overloaded. The latency maybe determined based on a variety of techniques. For example, receipt oftransmissions to the analytics platform by a monitored computinginstance may be confirmed by the analytics platform transmitting anacknowledgment signal to the monitored computing instance. Thetransmission to the analytics platform may include a transmissionidentifier, and the acknowledgment signal may reference thattransmission identifier, such that the throttle module 44 may identifywhich acknowledgment signal is associated with which transmission andcalculate a difference between the time at which the transmission wassent and the time at which the acknowledgment signal was received todetermine a latency. In other embodiments, the acknowledgment signal mayinclude data indicative of the time at which the acknowledgment signalwas received, or data requesting a delay.

The threshold may be a predetermined threshold or a dynamic thresholdthat changes based on any of a variety of factors. In some embodiments,the threshold is one of the obtained collector parameters describedabove with reference to step 88 of FIG. 4. The threshold, in someembodiments, may be adjusted based on an amount of data stored in thebuffer module 46 of FIG. 2. For example, the threshold may be increasedin response to an increase in the amount of data in the buffer, inresponse to the amount of data in the buffer exceeding some bufferthreshold, or some other factor. The threshold may be decreased based onsimilar factors decreasing.

Upon determining that the latency is greater than the threshold, inresponse, some embodiments of the process 100 may proceed to decisionblock 114, in which the process 100 may wait until a transmission delayhas elapsed before attempting to transmit additional metric data. Thedetermination of block 114 may be performed by the throttle module 44described above with reference to FIG. 2. In some embodiments, thetransmission delay may be a predetermined value or a dynamicallydetermined value that varies based on one or more factors. For example,the transmission delay may be adjusted along with the latency thresholdin the manner described above based on the amount of data stored in thebuffer module 46 of FIG. 2. In another example, the delay may beadjusted based on variability, such as a standard deviation, range, orvariance, of the latency of transmissions to the analytics platform, atechnique which is expected to exploit relatively frequent periods oflow latency intermingled with periods of higher latency.

Waiting until the transmission delay has elapsed is expected to throttledata received by the analytics-platform computing system 12, therebypotentially preventing the analytics-platform computing system 12 frombeing swamped by a spike in network traffic following a network outageand potentially avoiding the loss of data, without theanalytics-platform computing system 12 centrally controllingtransmission times. Further, such throttling is expected to protect theanalytics-platform computing system 12 from sudden burst of trafficduring a systemic failure, for example during a failure affectingmultiple monitored computing systems within a data center of a cloudservice provider. Throttling the transmission of metric data based onlatency is also expected to coordinate the operation of multiplecollectors across multiple monitored computing systems, withoutnecessarily requiring centralized control by the analytics-platformcomputing system 12 to coordinate the transmission of the variouscollectors. This is expected to reduce the complexity of configuring theanalytics platform and facilitate use of the analytics platform as aservice. Other embodiments, however, do not throttle network traffic orcentrally control transmission.

Upon determining that the transmission delay has not elapsed, theprocess 100 returns to block 114 and continues to wait. Alternatively,upon determining that the transmission delay has elapsed, the process100 of this embodiment proceeds to block 116. Similarly, in the decisionstep of block 112, upon determining that latency of transmissions to theanalytics platform is not greater than the latency threshold, theprocess 100 of this embodiment also proceeds to block 116.

Embodiments of the process 100 include transmitting metric data batchesto the analytics platform, as illustrated by block 116. Transmitting themetric data batch may include encoding the metric data batch in variousnetworking protocols. In some embodiments, the data may be encoded in afile transfer protocol, in a hypertext transfer protocol (e.g., HTTPSecure), or in SPDY, for instance.

Some embodiments of the process 100 include determining whether thetransmission was successful, as indicated by determination block 118.Determining whether the transmission was successful may includedetermining whether an acknowledgment signal is received from theanalytics platform indicating that the transmitted data was received. Insome embodiments, this determination may include determining whethersuch a signal is received within a timeout threshold. Upon determiningthat transmission was not successful, some embodiments of the process100 may return to decision block 112 in response. Alternatively, upondetermining that transmission was successful, in response, someembodiments of the process 100 may return to block 110, and additionaldata may be retrieved for transmission.

The processes 98 and 100 are expected to transmit metrics of themonitored computing instance in a manner that is relatively easy toconfigure, that is relatively robust to changes in network traffic andchanges in the capacity of the analytics-platform computing system 12 toprocess data, and is relatively unlikely to lose data. Not allembodiments, however, provide some or all of these benefits.

FIG. 7 illustrates details of an embodiment of the analytics-platformcomputing system 12 introduced in FIG. 1. In some embodiments, theanalytics-platform computing system 12 is a scalable cloud-basedcomputer system management program capable of providing computer systemmanagement as a service to a plurality of accounts each having computersystems with a plurality of monitored computing instances. Further, someembodiments of the analytics-platform computing system 12 may be capableof providing real-time or near real-time analyses and reports of theoperation of the monitored computing systems. Not all embodiments,however, provide some or all of these benefits.

Some embodiments of the analytics-platform computing system 12 areimplemented on a cloud computing system having a plurality of computinginstances and capable of provisioning additional computing instancesdynamically, for example based on load, a desired response time, orother factors. Such implementations are expected to reduce costsrelative to systems that statically include sufficient computing powerfor maximum expected loads, as such systems often include computingresources that remain unused for much of the time. However, embodimentsare not limited to cloud-based implementations or scalableimplementations.

In some embodiments, the analytics-platform computing system 12 includesone or more receive engines 120, one or more analytics engines 122, oneor more platform engines 124, one or more web user interface engines126, one or more service engines 128, and one or more database engines130. In some embodiments, the engines 120, 122, 124, 126, 128, and 130,or a subset thereof, may be modules of an application embodying theanalytics-platform, or in some embodiments, these engines 120, 122, 124,126, 128, and 130, or a subset thereof, may be separate processes, forexample separate concurrent processes executing on separate monitoringcomputing instances 26 or separate processes executing on the samemonitoring computing instance 26. In some embodiments, theanalytics-platform computing system 12 may be characterized as adistributed computing system in which the engines 120, 122, 124, 126,128, and 130 operate on separate virtual machines or separate physicalcomputers that may be co-located or may be geographically distributed.The engines 120, 122, 124, 126, 128, and 130 may be capable ofcommunicating with one another bi-directionally, for example via anetwork (such as a local or wide area network Ethernet connection, viathe Internet), via a bus or backplane of a computing device, viaparameters passed between software modules (such as values passed byreference or by copies), or through other techniques. Further, theanalytics platform computing system 12 may be capable of communicatingbi-directionally with the network 25, for example sending data to andreceiving data from the above-described collectors 30 and client devices20, 22, and 24.

The illustrated embodiment includes an equal number of each engine andthree of each engine 120, 122, 124, 126, 128, and 130, but otherembodiments may include different numbers of each engine relative to oneanother and relative to the number depicted in FIG. 7. For example, someembodiments may include additional database engines 130 that are addedin response to increases in the amount of data stored by theanalytics-platform computing system 12, increases in response to theamount of requests for data to be stored or retrieved from theanalytics-platform computing system 12, or other factors. Similarly,other engines 120, 122, 124, 126, or 128 may be added or removed basedon load, for instance based on response time, requests, commands, etc.

While the illustrated engines 120, 122, 124, 126, 128, and 130 and theircomponents described below are illustrated and described with referenceto discrete functional blocks, these components may be implemented inhardware or software that is intermingled, conjoined, subdivided, orotherwise differently organized.

In some embodiments, each of the engines 120, 122, 124, 126, 128, and130 may be executed on a monitoring computing instance 26 within anoperating system of the monitoring computing instance. And each of theengines 120, 122, 124, 126, 128, and 130 and the analytics-platformcomputing system 12 may receive data via a load balancer server, whichmay route tasks and data to various instances of the engines 120, 122,124, 126, 128, and 130 based upon unused capacity within these engines.

FIG. 8 illustrates additional details of an embodiment of the receiveengine 120 of FIG. 7. In this embodiment, the receive engine 120includes an input 132, a decryption module 134, a decompression module136, an account management module 138, a parser module 140, a queueoutput module 142, and an output module 144 to the database engine 130of FIG. 7. As described in greater detail below, the receive engine 120,in some embodiments, may be capable of receiving data from thecollectors 30 (FIG. 1), decrypting the received data, decompressing thereceived data, associating the received data with an account and with acomputing instance, parsing the received data, and outputting the parseddata to a queue for subsequent processing by the analytics engine 122and to the database engine 130 for storage in memory. The receive engine120 may also be capable of maintaining a session with one or morecollectors, associating the received data with the correspondingsession, and transmitting data (e.g., acknowledgement signals) to theappropriate collector of the corresponding session. In some embodiments,the receive engine may decode the above-described network transferprotocols and validate the status of an account and credentialsassociated with the account for the monitored computing system, forexample by querying the service engine 128 for a subscription status anddetermining whether a subscription is current or lapsed. Someembodiments may not process data that is received without acorresponding active subscription.

Some embodiments may include one instance of the receive engine persession, or other embodiments may include a single receive engine thatprocesses multiple sessions. In certain embodiments, sessions may bemanaged by the platform engine 124 or the service engine 128 describedbelow, and the receive engine 120 may receive data that is alreadyassociated with a session or a corresponding collector.

In some embodiments, the decryption module 134 may receive data from theinput 132, such as encrypted metric batches from the collectors anddecrypt the received data. In some embodiments, the receive engine 120may obtain a decryption key associated with the corresponding collector,monitored computing instance, monitored computing system, or account(e.g., from the service engine 128), and the decryption engine 134 maydecrypt data based on this obtained (e.g., received) encryption key.

The decryption module 134 may output the decrypted data to thedecompression module 136, which may decompress the received data, suchas the received metric batches from the collectors 30. Decompression mayinclude identifying strings in the decrypted data corresponding tolarger patterns in the uncompressed metric data and replacing theidentified strings with the corresponding larger pattern. In someembodiments, data indicative of these patterns and the correspondingidentifying strings may be transmitted to the receive engine from thecollector or from the platform engine 124.

The decompressed data may be transmitted from the decompression module136 to the account management module 138, which may associate thedecompressed data with an account, a monitored computing system, or amonitored computing instance (for example with each of these entities).In some embodiments, the account management module may attach metadatato the decompressed data indicating the association. Some embodiments ofthe account management module 138 may also retrieve or otherwise obtainconfiguration data of the collector 30 indicative of the formatting ofthe metric data batches transmitted from the collector 30. For example,the account management module 138 may obtain data indicating delimitersand which fields are transmitted in which sequence and, in response, theaccount management module 138 may label the uncompressed data withmetadata indicating the corresponding fields, for example by insertingXML tags and attributes or JSON names for name-value pairs and removingdelimiters.

The output of the account management module 138 may be transmitted tothe parser module 140, which may parse the received data. The input tothe parser module 140 may be a serialized data-structure, e.g., adocument or string expressed in XML or JSON. In some embodiments, theparser 140 may de-serialize the input data into a hierarchical or graphdata structure held in random access memory, such as a tree, an objectwithin an object oriented programming environment, a multi-dimensionalarray, or the like. In some embodiments, the parser module 140 may parsethe received data into a data structure that, when accessed with theappropriate tools, can be queried, iterated through, or otherwiseinterrogated. A de-serialized data structure is expected to providefaster analysis and storage of data than a serialized string ordocument, as data can be accessed and manipulated without potentiallyhaving to iterate through every character of the string or document,though some embodiments may leave the data in a serialized format orsome other format.

The output of the parser 140 may be transmitted to the queue outputmodule 142 and the output module 144 to the database engine 130 (FIG.7). In some embodiments, the outputs 142 and 144 may be separateprocesses or separate threads that output data during overlapping timeperiods, for instance concurrently or approximately concurrently.Outputting the data in parallel is expected to reduce the time betweenwhen data is first received and when analyses and results of the dataare reported to users, though not all embodiments necessarily providethis benefit. Indeed, some embodiments may not output data to differentdestinations or may not output data in parallel, which is not to suggestthat any other feature described herein is required in every embodiment.In some embodiments, the queue output module 142 may transmit thereceived data to a buffer (e.g., a queue) from which the subsequentlydescribed analytics engine 120 pulls tasks or to a queue in the platformengine 124 that assigns tasks to the analytics engine 120. The outputmodule to the database engine 130 may be capable of transmitting thereceived data to the database engine 130 and instructing the databaseengine 130 to write the data to memory.

An embodiment of the analytics engine 122 is shown in greater detail inFIG. 9. In some embodiments, the analytics engine may include aplurality of analysis functions, examples of which are described below,that vary according to the priority of their activities. The analyticsengine may receive signals (including metric data) from the receiveengine 120, for example signals from the queue output module 142indicating that data is available to be analyzed or other tasks areavailable to be performed, or some embodiments of the analysis engine122 may include a set of processes or threads that remove tasks from aqueue hosted by the platform engine 124. Some embodiments may includeone analysis engine per session with a collector, one analysis enginefor multiple sessions, one analysis engine per monitored computingsystem, one analysis engine per account, or one analysis engine formultiple monitored computing systems, depending upon the computing loadand the computing power of the analysis engine 122.

In some embodiments, the analysis engine 122 may include a metric datainput/output 146, a command input/output 148 by which new commands ortasks are identified or transmitted, a plurality of window analyzers150, 152, and 154, and a plurality of new task flags 156, 158, and 160that may signal the availability of new collections of data to beprocessed to each of the window analyzers 152 through 154, as describedin greater detail below.

The window analyzers 150, 152, and 154 may each be configured to analyzea different temporal window of data, for example window analyzer 150 maybe configured to analyze 20-second windows of data, the window analyzer152 may be configured to analyze 10-minute windows of data, and thewindow analyzer 154 may be configured to analyze one-month windows ofdata. Details of the operation of the window analyzers 150, 152, and 154described in greater detail below with reference to FIG. 12. The windowanalyzers 150, 152, and 154 may receive data from the database engine130 by transmitting queries to the database 130 or may receive datadirectly from the receive engine 120 via the input/output path 146.Similarly, the window analyzers 150, 152, and 154 may write results tothe database engines 130 by transmitting results and write commands viathe input/output path 146 to the database engines 130.

The operation of the window analyzers 150, 152, and 154 may be stagedsuch that each window analyzer 150, 152, and 154 triggers the nextwindow analyzer when the appropriate time for that next window analyzerto run occurs, for example when the next window of the adjacent windowanalyzer starts. In some embodiments, window analyzers 152 through 154may be started based on a signal from a window analyzer tasked withanalyzing a smaller window, the signal indicating that a new instance ofthe larger window has started. Starting window analyzers in thisfashion, based on signals from more frequently operated windowanalyzers, is expected to conserve computing power and reduce the degreeto which the operation of a process or thread analyzing one monthwindows of data, for example, interferes with the operation of processesor threads analyzing shorter windows of data. This technique is expectedto expedite results from the first window analyzer 150, resulting inreal-time or near real-time reporting of analyses of received metrics ofmonitored computing instances. Not all embodiments, however, providethis benefit or use this technique. For example, some embodiments mayoperate separate processes or threads for each of the window analyzers150, 152, and 154 that operate generally continually and generallyconcurrently, e.g., an analysis for the trailing one-month window may begenerally continually updated, rather than being updated once per-month.

Each window analyzer 150, 152, and 154 includes one or more statisticscalculators 162 and one or more criteria evaluators 164. In operation,upon instantiation of each of the window analyzers 150, 152, 154 or upona signal indicating that a window has closed or is near closing, eachwindow analyzer 150, 152, and 154 may transmit a request to the databaseengine 130 for data measured within that closing window, data thatarrived within that window, or results of calculations by other windowanalyzers 150, 152, and 154 based on such data (thereby reducing theamount of data requested and speeding operation). In some embodiments,the statistics calculators 162 may calculate statistics based on theresults of the request. For example, statistics calculators 162 maycalculate a maximum, a minimum, an average, a median, a mode, a count, astandard deviation, a range, a variance, or other statistics. Similarly,the criteria evaluators 164 may evaluate the data received from thequery against various criteria, such as whether thresholds are crossed,whether certain trending rules have been satisfied (e.g., five or moreconsecutive increasing data points or two out of three data pointsoutside of three standard deviations from a mean), or whether variousstates have obtained in the monitored computing instances, such aswhether various error conditions have occurred in the monitoredcomputing instances.

In some embodiments, window analyzers 152 through 154 may calculatestatistics and evaluate criteria based on the result of calculatedstatistics or evaluated criteria from more frequently operated windowanalyzers. For example, window analyzer 152 may retrieve from thedatabase engine 130 the results of statistics calculated by the firstwindow analyzer 150. Retrieving results from other window analyzers isexpected to reduce the amount of data processed by each of the windowanalyzers and speed operation of the analytics engine 122. However, someembodiments may retrieve all data received within an analyzed window forsome or all of the calculated statistics or evaluated criteria withinsome or all of the windows.

Upon calculating statistics and evaluating criteria, the results may bewritten to the database engine 130. The results may include statisticsby which various data visualizations, such as charts, may be formed andbinary outputs, such as alarms. The window analyzers 150, 152, and 154may also determine whether the next longer window has closed or is aboutto close. Upon determining that the next longer window has closed or isabout to close, the window analyzers 150, 152, or 154 may set a new taskflag 156, 158, or 160 for the next longer window analyzer, and inresponse, the next longer window analyzer 152 through 154 may begin ananalysis based on the change in state of the new task flag 156, 158, or160. By way of example, first window analyzer 150 may determine that awindow to be analyzed by the second window analyzer 152 has closed, andin response, first window analyzer 150 may set new task flag 156 totrue. In response to this change in new task flag 156, the second windowanalyzer 152 may begin analyzing the next longer window and reset thenew task flag 156 to false. This process may be repeated for each of thewindow analyzers 152 through 154. The first window analyzer 150 mayanalyze each metric data batch received from the receive engine 120, orthe first window analyzer 150 may receive commands from the platformengine 124, for example, indicating that a new window is ready foranalysis. In other embodiments, a separate process or thread, such as ajob scheduler operated by the platform engine 124 may schedule tasks forthe window analyzers 150, 152, and 154. These tasks and other commandsmay be communicated to the window analyzers 150, 152, and 154 via thecommand input/output 148.

In some embodiments, the analytics engine 122 may be capable ofobtaining an account identifier, an identifier of a monitored computinginstance, or an identifier of a monitored computing system associatedwith the data to be analyzed, and based on these identifier(s) obtainuser-configurable statistics, criteria, and window periods by which thedata is to be analyzed. In some embodiments, analysis criteria may bestored in the database engine 130 and indexed according to an accountidentifier, an analysis identifier, a monitored computing instanceidentifier, or a monitored computing system identifier. Some embodimentsmay receive analysis specifications from users, for example via theclient devices 20, 22, and 24, and the statistics calculators 162,window durations, and the criteria evaluators 164 may be configured toperform the requested calculations and criteria evaluations.

An embodiment of the web user interface engine 126 is illustrated ingreater detail with reference to FIG. 10. The web user interface engine126 may be configured to interface with client devices 120, 122, and 124of FIG. 1, for example by providing an interface by which users of theanalytics platform may monitor the performance of monitored computingsystems and configure the operation of the analytics-platform computingsystem 12.

In some embodiments, the web user interface engine 126 may include anapplication program interface server 162, a web server 164, and ahypertext transport protocol secure service module 166. The HTTPS module166 may encode and decode commands and data for transmission via anetwork protocol, such as the network protocols described herein, viathe network 25 to and from the client devices 20, 22, and 24. In someembodiments, the web user interface engine 126 may be capable ofvalidating credentials and accounts for users attempting to interfacewith the analytics-platform computing system 12. For example, the webuser interface engine 126 may be operative to transmit request to theservice engine 128 including user provided account identifiers andcredentials and selectively allow access to particular account databased on whether the service engine 128 indicates the accountidentifiers and credentials are valid and whether a subscription iscurrent.

The application program interface server 162 may be a server capable ofparsing calls to the application program interface received over thenetwork 25, for example from client devices 20, 22, or 24, and executingcommands requested by the calls. For example, the API server 162 may becapable of querying data from the database engine 130 based on API callsrequesting such a query, changing the configuration of monitoring oranalyses of metrics based on API calls requesting such a change, orperform other tasks.

The web server 164 may be operative to generate instructions (e.g.,instructions encoded in HTML, CSS, and JavaScript) for forming a userinterface on the client devices 20, 22, and 24, such as a viewport of abrowser displaying data visualizations of various metrics, statistics,and criteria evaluation results associated with various computinginstances, monitored computing systems, or accounts. The web server 164may also be capable of outputting a interactive user interface by whichusers may enter commands, for example by clicking, dragging, touching,speaking, or otherwise interacting with the client devices 20, 22, 24,and the web server 164 may be capable of responding to these commands byrequesting additional data or different data and instructing a change inthe user interface responsive to the command.

The web user interface engine 126 is expected to facilitate interactionswith the analytics-platform computing system 12 by users who use theanalytics-platform computing system 12 as a service, rather thanoperating their own instance of the analytics-platform computing system12, thereby potentially reducing labor and equipment costs associatedwith monitoring a computing system. Other embodiments, however, may havea special-purpose application for displaying results and configuring theanalytics-platform computing system 12.

An embodiment of the platform engine 124 is illustrated in greaterdetail in FIG. 11. In some embodiments, the platform engine 124 may becapable of coordinating some or all of the operation of the otherengines 120, 122, 126, 128, and 130, as described below. In someembodiments, the platform engine 124 includes an update manager module168, a scheduler module 170, a database maintenance module 172, and aninstance manager 174.

The update manager module 168 may be operative to cooperate with thecollector updater module 40 described above with reference to FIG. 2 tomanage the version of collectors executed by monitored computinginstances. In some embodiments, the update manager 168 may be operativeto receive data indicative of the current version of a collectorexecuted by a monitored computing instance, determine whether thecurrent version is the latest version or is a version specified by auser of an account associated with the monitored computing instance, andin response to determining that the current version is not the correctversion, transmit the correct version to the monitored computinginstance. In other embodiments, the update manager 168 may be capable ofreceiving a request for data indicative which version is correct,identifying the correct version, and if requested by a collector, thetransmitting the correct version to the requesting entity, which mayitself determine whether to upgrade.

In some embodiments, the platform engine 124 includes the scheduler 170,which may schedule operations of the window analyzers 150, 152, 154. Insome embodiments, the scheduler 170 schedules the operation of thewindow analyzer 150, for example by signaling that a new window of datais available to be analyzed, and the other window analyzers 152 through154 may begin their analyses based on the new task flags 156 through160. Or in some embodiments, the scheduler 170 may schedule theoperation of more, or all, of the window analyzes 150, 152, and 154.

The database maintenance module 172, in some embodiments, may coordinateand schedule certain activities of the database engine 30. For example,the database maintenance module 172 may schedule or coordinate theremoval of data within the database engine 130 that is older than somedate threshold and certain activities to improve performance, forexample indexing of the database.

The instance manager 174, in some embodiments, may scale theanalytics-platform computing system 12, for example, automatically,based on need for additional resources. In some embodiments, theinstance manager 174 may periodically, or on some other schedule,determine a response speed of the analytics-platform computing system 12to certain tasks, determine an amount of data received or analyzed bythe analytics-platform computing system 12, determine a number ofmonitored computing instances or monitored computing systems, or somecombination thereof, and based on this determined data, the instancemanager 174 may request additional instances of various engines 120,122, 124, 126, 128, or 130 or terminate such instances. The instancemanager 174 may include machine images including an operating system andapplications for instantiating the various engines 120, 122, 124, 126,128, and 130. Automatically scaling the analytics-platform computingsystem 12 based on need is expected to reduce the cost of operating theanalytics-platform computing system 12, as resources are procured asneeded rather than being purchased and operated in anticipation of aworst-case scenario. However, some embodiments do not automaticallyscale, or other embodiments may scale automatically but provide otherbenefits.

As noted above with reference to FIG. 7, some embodiments of theanalytics-platform computing system 12 may include the service engine128. The service engine 128 may contain components related to customeraccounting. For example, account identifiers, credentials associatedwith accounts, collector configurations associated with accounts, andanalysis configurations associated with accounts. The service engine mayalso include data indicative of subscriptions, such as data indicativeof account balances, data indicative of service-level agreements, dataindicative of account duration, and data indicative of costs. Theservice engine may also be operative to generate reports based on theseaccounts and signal other components of the analytics-platform computingsystem 12 when such components are in need of data indicative of theaccounts or account related data.

The database engine 130, in some embodiments, may be a relational or anon-relational database. Non-relational databases are expected toprovide certain benefits relating to the speed, flexibility, and thescalability of the analytics-platform computing system 12. In someembodiments, the database engine 130 hosts a non-relational databasewithout external load-balancing that is schema free, or is capable ofstoring data in non-predetermined fields and organization. Someembodiments may include a database capable of storing data in the formof documents, rather than in the form of tables, such as XML documentsor JSON documents.

In some embodiments, the database engine includes an instance of MongoDB or other non-relational databases. For example, some embodiments mayinclude a non-relational database that organizes data hierarchically, ina tree structure, or a data structure in which nodes have a parent andchild relationship with each child having only one parent, but someparents potentially having multiple children. For instance, the field“processors” may be a node, with multiple child fields named“processor,” one for each processor, each of which may have child nodesnamed “processor usage,” “processor temperature,” and “processes.” Someembodiments may store the data in a network model, for example as agraph database in which child nodes are not limited to a single parentnode.

A non-relational database is expected to be relatively flexible, as therelationship between various stored fields need not necessarily bepredefined by a user to begin collecting data, and a non-relationaldatabase is expected to scale relatively readily. However, embodimentsare not limited to the above-described non-relational databases. Someembodiments may include a relational database, a memory image, adocument repository, or other organization of data.

FIG. 12 illustrates an example of a process 176 for analyzing datareceived from monitored computing instances. The process 176, in someembodiments, may be performed by the analytics engine 122 describedabove with reference to FIG. 9, but embodiments of the process 176 arenot limited to this configuration. In this embodiment, the process 176begins with determining whether a first window has elapsed, as stated bydecision block 178. Upon determining that a first window has notelapsed, the process 176 continues to wait and the determination 178 isrepeated. In some embodiments, the first window of decision block 178may be a shortest window of the windows analyzed by the process 176, forexample a window of less than or approximately equal to 2 minutes, 1minute, 30 seconds, 20 seconds, 10 seconds, 5 seconds, one second, or ahalf second. In some embodiments, a determination that the first windowhas elapsed may be made in response to the arrival of a batch of metricscollected during a time period corresponding to the first window by acollector.

Upon determining that the first window has elapsed, in response, theprocess 176 may proceed to obtain metrics measured within the window, asindicated by block 180, and calculate statistics based on the obtainedmetrics, as indicated by block 182. These steps 180 and 182 may beperformed by the window analyzer 150 described above with reference toFIG. 9, in some embodiments. The metrics may be obtained by querying adatabase or receiving a parallel flow of metrics data transmitted to thewindow analyzer 150. The statistics may be calculated with theabove-described statistics calculator module 162, in some embodiments.

The process 176 may also include storing the calculated statistics, asindicated by block 184, evaluating criteria based on obtained metrics,as indicated by block 186, and storing results of the evaluation, asindicated by block 188. The criteria may be evaluated with the criteriavaluator modules 164 described above with reference to FIG. 9, and thestored statistics and results of the evaluation may be stored by theabove-described database engine 130.

Some embodiments of the process 176 may include determining whether anext-longer window has elapsed, as indicated by decision block 190.Determining whether a next-longer window has elapsed may includecomparing a value indicative of the beginning of the next-longer windowto a current time and determining whether the difference isapproximately equal to or greater than a threshold of the duration ofthe next longest window. In some embodiments, the first window analyzer150 of FIG. 9 may determine whether the window to be analyzed by thesecond window analyzer 152 has elapsed in the decision block 190. Upondetermining that the next-longer window has elapsed, in response, theprocess 176 may proceed to start an analysis of the next longer window,as indicated by initiation block 192. Alternatively, upon determiningthat the next longer window has not elapsed, the process 176 may returnto decision block 178.

As indicated by initiation block 192, the process 176 may includestarting a sub process for analyzing the next longer window. Analyzingthe next longer window may include analyzing metrics of monitoredcomputing instances that arrive during (or were measured during) thenext longer window, for example during the window to be analyzed bywindow analyzer 152 of FIG. 9.

The process 176 includes, in some embodiments, upon the start ofinitiation block 192, obtaining calculated statistics and results ofcriteria evaluated within the new window, or the next longer window thatinitiated the process block 192, as indicated by block 194. For example,multiple instances of the window analyzed by the first window analyzer150 may occur during the window analyzed by the second window analyzer152, and the results of these multiple analyses may be obtained in step194, for instance by querying the database engine 130. In someembodiments, the metric data obtained from the collector may also beobtained in step 194. After obtaining this data, some embodiments ofprocess 176 include calculating statistics based on the obtained data,as indicated by block 196 storing the calculated statistics, asindicated by block 198, evaluating criteria based on the obtained data,as indicated by block 200, and storing the results of the evaluation, asindicated by block 202. These steps 196, 198, 200, and 202 may beanalogous to, or identical to, those performed in steps 182, 184, 186,and 188 and may be performed, for example by the second window analyzer152 through the nth window analyzer 154, depending upon the identity ofthe next longer window, for example whether the next longer window isthe window corresponding to the second window analyzer 152, a thirdwindow analyzer, or the nth window analyzer 154.

Some embodiments of process 176 further include determining whether thenext longer window has elapsed (relative to the window analyzed in steps194, 196, 198, 200, and 202), as indicated by decision block 204. Forexample, in a use case in which the steps 194-202 are evaluated for datacorresponding to a window of the second window analyzer 152, adetermination may be made whether the window corresponding to the thirdwindow analyzer has elapsed, and during an iteration of steps 194through 202 in which the third window analyzer window is analyzed, adetermination may be made in decision block 204 whether a windowcorresponding to a fourth window analyzer has elapsed, and so on. Upondetermining that the next longer window has elapsed, the process 176 mayreturn to (e.g., recurs to, or initiate a parallel thread or process)initiation block 192, and steps 194 through 204 may be repeated from theperspective of the next longer window, analyzing data that arrive duringthe next longer window and determining whether the next longer windowafter that window has elapsed. Upon determining that the next longerwindow has not elapsed, in response, the process 176 may return todecision block 178.

The process 176, particularly when used in combination with theabove-described embodiments of a database engine 130 based on anon-relational database, is expected to facilitate real-time or nearreal-time displays of, and alerts to, data indicative of the operationof monitored computing instances. For example, some embodiments may becapable of displaying statistics indicative of a change in the operationof a monitored computing instance within an amount of time approximatelyequal to or less than 2 minutes, 1 minute, 30 seconds, 20 seconds, 10seconds, 5 seconds, one second, or a half second of a change. Thisreal-time or near real-time response is helpful for users attempting toverify whether a cloud service provider hosting a monitored computingsystem is meeting a service level agreement. Service-level agreementsoften specify uptimes on the order of 99.999% uptime, or similar amountsof uptime, and verifying whether this agreement has been met is ofteneasier when real-time, relatively high-resolution data indicative of theoperation of monitored computing instances is available, as relativelyshort interruptions or decreases in performance are more likely to bedepicted in a visualization of performance in a user interface ordetected with an alarm. Not all embodiments, however, necessarilyprovide this benefit or provide real-time or near real-time results.

In some embodiments, the computing instances described herein may beexecuted by a computing device (for example, as the computing deviceitself or as a virtual machine hosted by the computing device) describedbelow with reference to FIG. 13. Further, the modules, applications, andvarious functions described above may be implemented by such computingdevices having instructions for executing these acts stored in atangible, non-transitory machine readable medium, e.g., memory, andhaving one or more processors that, when executing these instructions,cause the computing devices to perform the above-described acts.

FIG. 13 is a diagram that illustrates an exemplary computing device 1000in accordance with embodiments of the present technique. Variousportions of systems and methods described herein, may include or beexecuted on one or more computer devices similar to computing device1000. Further, processes and modules described herein may be executed byone or more processing devices similar to that of computing device 1000.

Computing device 1000 may include one or more processors (e.g.,processors 1010 a-1010 n) coupled to device memory 1020, an input/outputI/O device interface 1030 and a network interface 1040 via aninput/output (I/O) interface 1050. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingdevice 1000. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may receive instructions and data from a memory (e.g., systemmemory 1020). Computing device 1000 may be a uni-processor deviceincluding one processor (e.g., processor 1010 a), or a multi-processordevice including any number of suitable processors (e.g., 1010 a-1010n). Multiple processors or multi-core processors may be employed toprovide for parallel or sequential execution of one or more portions ofthe techniques described herein. Processes, such as logic flows,described herein may be performed by one or more programmable processorsexecuting one or more computer programs to perform functions byoperating on input data and generating corresponding output. Processesdescribed herein may be performed by, and apparatus can also beimplemented as, special purpose logic circuitry, e.g., an FPGA (fieldprogrammable gate array) or an ASIC (application specific integratedcircuit). Computing device 1000 may include a plurality of computingsub-devices (e.g., distributed computer systems) to implement variousprocessing functions.

I/O device interface 1030 may provide an interface for connection of oneor more I/O devices 1060 to computing device 1000. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1060 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1060 may be connected to computing device 1000through a wired or wireless connection. I/O devices 1060 may beconnected to computing device 1000 from a remote location. I/O devices1060 located on remote computer system, for example, may be connected tocomputing device 1000 via a network and network interface 1040.

Network interface 1040 may include a network adapter that provides forconnection of computing device 1000 to a network. Network interface may1040 may facilitate data exchange between computing device 1000 andother devices connected to the network. Network interface 1040 maysupport wired or wireless communication. The network may include anelectronic communication network, such as the Internet, a local areanetwork (LAN), a wide area (WAN), a cellular communications network orthe like.

System memory 1020 may be configured to store program instructions 1100or data 1110. Program instructions 1100 may be executable by a processor(e.g., one or more of processors 1010 a-1010 n) to implement one or moreembodiments of the present techniques. Instructions 1100 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1020 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude, non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1020 may include a non-transitory computer readablestorage medium may have program instructions stored thereon that areexecutable by a computer processor (e.g., one or more of processors 1010a-1010 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., device memory 1020) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). In some embodiments, the program may beconveyed by a propagated signal, such as a carrier wave or digitalsignal conveying a stream of packets.

I/O interface 1050 may be configured to coordinate I/O traffic betweenprocessors 1010 a-1010 n, device memory 1020, network interface 1040,I/O devices 1060 and/or other peripheral devices. I/O interface 1050 mayperform protocol, timing or other data transformations to convert datasignals from one component (e.g., device memory 1020) into a formatsuitable for use by another component (e.g., processors 1010 a-1010 n).I/O interface 1050 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Some embodiments of the techniques described herein may be implementedusing a single instance of computer system 1000, or multiple computersystems 1000 configured to host different portions or instances ofembodiments. Multiple computer systems 1000 may provide for parallel orsequential processing/execution of one or more portions of thetechniques described herein.

Those skilled in the art will appreciate that computing device 1000 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computing device 1000 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computing device 1000 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or the like. Computing device 1000may also be connected to other devices that are not illustrated, or mayoperate as a stand-alone device. In addition, the functionality providedby the illustrated components may in some embodiments be combined infewer components or distributed in additional components. Similarly, insome embodiments, the functionality of some of the illustratedcomponents may not be provided or other additional functionality may beavailable.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computing device 1000 may be transmitted to computingdevice 1000 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network or a wireless link. Various embodiments may furtherinclude receiving, sending or storing instructions or data implementedin accordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present invention may be practiced with othercomputer system configurations.

It should be understood that the description and the drawings are notintended to limit the invention to the particular form disclosed, but tothe contrary, the intention is to cover all modifications, equivalents,and alternatives falling within the spirit and scope of the presentinvention as defined by the appended claims. Further modifications andalternative embodiments of various aspects of the invention will beapparent to those skilled in the art in view of this description.Accordingly, this description and the drawings are to be construed asillustrative only and are for the purpose of teaching those skilled inthe art the general manner of carrying out the invention. It is to beunderstood that the forms of the invention shown and described hereinare to be taken as examples of embodiments. Elements and materials maybe substituted for those illustrated and described herein, parts andprocesses may be reversed or omitted, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a”, “an”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “anelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements. The term “or”is, unless indicated otherwise, non-exclusive, i.e., encompassing both“and” and “or.” Terms relating to causal relationships, e.g., “inresponse to,” “upon,” “when,” and the like, encompass both causes thatare a necessary causal condition and causes that are a sufficient causalcondition, e.g., “state X occurs upon condition Y obtaining” is genericto “X occurs solely upon Y” and “X occurs upon Y and Z.” Similarly,unless otherwise indicated, statements that one value or action is“based on” another condition or value encompass both instances in whichthe condition or value is the sole factor and instances in which thecondition or value is one factor among a plurality of factors. Unlessspecifically stated otherwise, as apparent from the discussion, it isappreciated that throughout this specification discussions utilizingterms such as “processing”, “computing”, “calculating”, “determining” orthe like refer to actions or processes of a specific apparatus, such asa special purpose computer or a similar special purpose electronicprocessing/computing device. In the context of this specification, aspecial purpose computer or a similar special purpose electronicprocessing or computing device is capable of manipulating ortransforming signals, for instance signals represented as physicalelectronic, optical, or magnetic quantities within memories, registers,or other information storage devices, transmission devices, or displaydevices of the special purpose computer or similar special purposeprocessing or computing device.

What is claimed is:
 1. A monitored computing system configured totransmit metrics to an analytics platform, the monitored computingsystem comprising: a plurality of monitored computing-instances, eachmonitored computing-instance comprising: one or more memories; and oneor more processors, wherein one or more of the one or more memoriescomprises collector code, the collector code, when executed by one ormore of the one or more processors, being capable of causing themonitored computing-instance to perform steps comprising: initiating amonitoring session with an analytics platform by transmitting a signaloperable to initiate a monitoring session with the analytics platform toan Internet Protocol address of the analytics platform; obtainingmetrics of the monitored computing-instance; and transmitting theobtained metrics of the monitored computing-instance to the analyticsplatform.
 2. The monitored computing system of claim 1, wherein thesteps further comprise: storing the obtained metrics in a buffer; andwherein transmitting the obtained metrics comprises transmitting themetrics stored in the buffer.
 3. The monitored computing system of claim2, wherein transmitting the metrics stored in the buffer comprisesdelaying transmission.
 4. The monitored computing system of claim 3,wherein delaying transmission comprises delaying transmission based on alatency of previous transmissions to the analytics platform.
 5. Themonitored computing system of claim 3, wherein delaying transmissioncomprises throttling transmission of the metrics.
 6. The monitoredcomputing system of claim 1, wherein initiating a monitoring sessioncomprises determining an identifier of the monitored computing-instanceprior to communicating with the analytics platform.
 7. The monitoredcomputing system of claim 6, wherein determining an identifier of themonitored computing-instance comprises: before establishing a monitoringsession, determining a unique identifier based on properties of themonitored computing-instance; and wherein establishing a monitoringsession comprises: transmitting to the analytics platform an identifierof a monitored computing system including the plurality of monitoredcomputing-instances.
 8. A method of operating an analytics platform,comprising: receiving metrics of computing instances from a plurality ofdifferent computing-systems, each computing-system comprising aplurality of computing instances; determining, with a processor,statistics of the computing-instances based on the received metrics; andtransmitting the determined statistics within less than 120 seconds ofreceiving at least some of the metrics upon which the statistics arebased, to one or more user-devices, each of the one or more user-devicesbeing associated with at least one of the different computing-systems.9. The method of claim 8, wherein transmitting the determined statisticscomprises transmitting the determined statistics within less than 20seconds of receiving at least some of the metrics upon which thestatistics are based.
 10. The method of claim 8, wherein determiningstatistics comprises: storing the received metrics in a non-relationaldatabase; and analyzing the received metrics by querying thenon-relational database for the received statistics and calculating thestatistics based on the query results.
 11. The method of claim 8,comprising: receiving new-instance signals from new computing instancesindicating that additional computing instances are were added to one ofthe plurality of different computing-systems; and determining statisticsfor the new computing instances in response to the received new-instancesignals and based on metrics from the new computing-instances.
 12. Themethod of claim 8, comprising automatically scaling the analyticsplatform by adding computing instances to the analytics platform andterminating computing instances from the analytics platform based on aload of the analytics platform.
 13. A tangible, non-transitory,machine-readable medium storing instructions that when executed by oneor more computer systems effectuate operations comprising: receiving,over a network, from a plurality of monitored computing instances,encrypted metric batches from collectors executed by the monitoredcomputing instances; decrypting the metric batches from the collectors;associating the metric batches with one or more accounts, each metricbatch being associated with one of the accounts; parsing the decryptedmetric batches from a serialized data format into hierarchical key-valuepairs each associated with a respective one of the accounts; adding theparsed hierarchical key-value pairs to an analysis queue of data to beanalyzed; retrieving the parsed hierarchical key-value pairs from thequeue; updating, with a processor, each of a plurality of statisticseach describing values of metrics over a different temporal window basedon the retrieved parsed hierarchical key-value pairs, the statisticsbeing associated with the same account with which the correspondingmetric batch is associated; and receiving a request from a user deviceto view statistics of metrics of monitored computing instances, therequest being associated with one of the accounts; and sending theupdated statistics associated with the account of the request to theuser device.
 14. The medium of claim 13, wherein the operationscomprise: receiving another request from another user device to viewstatistics of metrics of monitored computing instances, the otherrequest being associated with another one of the accounts; sending theupdated statistics associated with the other account of the otherrequest to the other user device for display, the account and the otheraccount being different accounts associated with different monitoredcomputing instances.
 15. The medium of claim 13, wherein the operationscomprise: determining, with a process analyzing a shorter temporalwindow, that a duration of time of a longer temporal window hastranspired and, in response, initiating an analysis of metric values ofthe longer temporal window.
 16. The medium of claim 13, wherein aplurality of statistics are calculated for each of the temporal windows,and wherein each of the statistics is compared against alarm criteria.17. The medium of claim 13, wherein the operations comprise:automatically increasing a number of instances of monitoring computingdevices in response to an increase in a rate of receiving the metricbatches.
 18. The medium of claim 13, wherein the operations comprise:validating a subscription associated with the account of the requestbefore sending the updated statistics; and billing the account.
 19. Themedium of claim 13, wherein the metrics batches comprise metric valuesindicative of processor usage, memory usage, and network usage by arespective monitored computing instance.
 20. The medium of claim 13,wherein the operations comprise: grouping the parsed hierarchicalkey-value pairs according to user-defined groups associated with thesame account as the parsed hierarchical key-value pairs; and calculatingstatistics of the groups.